Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network

arXiv:2402.18576v1 Announce Type: cross
Abstract: Decision making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN). The IW technique, inspired by …

abstract artificial arxiv benefits cs.ai cs.lg cs.ne decision decision making face forecasting framework future general government health making network neural network planning pso public public health risks study type